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Related Experiment Video

Updated: Jan 13, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

A ferroelectric-memristor memory for both training and inference.

Michele Martemucci^1,2, François Rummens², Yannick Malot²

¹Université Grenoble Alpes, CEA-Leti, Grenoble, France.

Nature Electronics

|October 29, 2025

Summary

Researchers developed a novel unified memory stack for edge artificial intelligence (AI). This hybrid memory enables efficient inference and learning, overcoming limitations of current technologies for AI hardware.

Keywords:

Electrical and electronic engineering Electronic devices

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Area of Science:

Materials Science
Computer Engineering
Artificial Intelligence

Background:

Edge artificial intelligence (AI) demands energy-efficient inference and learning capabilities.
Existing memory technologies face limitations in endurance, programming energy, and read processes, hindering AI development.
A unified memory solution is needed to combine the strengths of different memory types for AI applications.

Purpose of the Study:

To develop a novel unified memory stack integrating memristor and ferroelectric capacitor functionalities.
To address the limitations of current memory technologies for energy-efficient edge AI.
To demonstrate an on-chip learning solution using the hybrid memory array.

Main Methods:

Fabrication of a unified memory stack using silicon-doped hafnium oxide and a titanium scavenging layer.
Integration into a complementary metal-oxide-semiconductor (CMOS) back end of line process.
Creation of an 18,432-device hybrid array with on-chip CMOS periphery circuits.

Main Results:

The unified memory stack successfully functions as both a memristor and a ferroelectric capacitor.
A hybrid array demonstrated efficient weight transfer between memory types without a digital-to-analogue converter.
The on-chip learning solution achieved competitive performance with software models on benchmarks.

Conclusions:

The developed unified memory stack offers a promising solution for energy-efficient edge AI.
This hybrid memory architecture overcomes key limitations of existing technologies for AI hardware.
The demonstrated on-chip learning validates the potential of this approach for future AI systems.